One of the hardest parts of building a truly innovative company is understanding what success looks like. Startups are unconventional by nature, so what makes for a useful comparison? Which metric should be your north star to see if you are on track?

The most obvious measure of growth is revenue, and in this article we’ll look in detail at what ‘fast revenue growth’ looks like, what is normal, and at which stages of development the fastest growth happens. Previously, we’ve covered what average forecasted growth looks like for startups (also included in the charts below) so here we’ll be looking at historical data from real companies.

Specifically, we’re looking at revenue over the first five years of a startup’s life, in terms of dollar amount and percentage increase. The sources in this article include some outstanding research by Hockey Stick Principles, revenue data on CBInsights, some independent research, as well as Equidam’s own database.

In addition to helping to shape your vision for what success looks like for a startup, this information applies directly to how you will build your revenue projections and determine future fundraising targets. By understanding the track record of these well known companies, you can reflect on the future vision of your own venture.

Understanding what success looks like

Startups are unconventional by nature, so averages aren’t always as helpful as they may seem.

For example, in a power law driven world of venture capital, there is a gulf in terms of performance between the few ‘winners’ and the many ‘losers’. Any startup aiming for the ‘average’ performance of those two groups is going to be a ‘loser’ by default.

Many of the fastest growing companies were a product of a particular combination of timing and technology. The rise of Netflix with the growth of streaming content and home broadband. The opportunity that Slack found, as the communication platform for tech-oriented organizations. They each have their own growth story, which can’t necessarily be emulated.

It’s not always reasonable to compare yourself to these companies, but you should be prepared for the fact that this is the level of success that some investors (venture capitalists, particularly) are looking for. For many entrepreneurs, success is more moderate and more reasonable benchmarks can be applied.

The first year is key

For startups, the first 12 months can be make-or-break for break for a number of reasons. It’s typically the period that you overcome the main threat to the company, whether that’s a product risk (can it be built?), a people risk (is the team right?), or a customer risk (can it generate revenue?). This volatility is represented in the variance of growth rates at this stage, though none of the exemples cited here are slow by any measure.

Situationally, some companies will start out with the product and the people proven (consider Facebook, starting out with a viral product built in a dorm room by some friends), and so revenue will be their first focus. Others will be scrambling to prove that the idea is viable before they even have the ability to monetise it at scale. See Airbnb, and their scrappy beginnings renting out air mattresses and selling cereal. The difference between those two examples is that while Facebook has 10x the revenue ($) of Airbnb in year 2, Airbnb has about 4x the growth rate (%) because it started out with so little.

It’s also worth noting that few companies have published revenue numbers for their early years, and there is a survivor bias towards companies that were successful enough for people to care about a retrospective on their growth.

As a benchmark, we have the golden rule from Paul Graham of Y Combinator, who states that a good startup going through Y Combinator will be aiming for 5-7% growth per week, while exceptional performance is closer to 10%. As we can see in the table below, not even the household names of tech have come close to that 10% performance, though a few have managed to exceed the 7% rate.

The data on startup growth

An interesting insight observation from looking at growth both in terms of dollar amount ($) and rate (%), is the position of Google – which started out both with strong initial revenue as well as an impressive growth rate. Amazon, by comparison, was top of the pile for total and growth in the first year, as well as a total in the final year.

There’s a good case to be made, for both of these examples, that they are an example of the extreme product:market fit that came about because of their position as market leaders in the initial dotcom boom. It would perhaps take an equally transformative moment in technology (AI, perhaps) to replicate that kind of success.

Airbnb, on the other hand, demonstrates incredible tenacity – starting out with less revenue than any other example, but maintaining a growth rate which outstrips everyone else. While it finishes behind its big-tech peers, it does exceed our aggregate examples. This, perhaps, is down to their need to carve their path into an existing industry, with huge competition but a more well-known market.

An interesting observation here is that both Google and Facebook suffered from a real drop-off in growth rate between their third and fourth years. This may reflect the transition point between domestic and international markets, moving beyond their core customer type, or the slowing of those initial network effects. Importantly, it’s a lesson that growth is not as linear as you might expect, and even the most successful companies will hit plateaus. Changing that trajectory may require a tweak to product strategy to open a new audience, or a change in growth strategy to reach a new market. These are important moments to consider for the future of your own company.

Conversely, only AirBnB and Equidam’s sample were able to accelerate their growth after the fall from that initial year one high. This may reflect the companies operating in more traditional markets, slowly building acceptance and solidifying their position in a more accessible market.

Looking at the total dollar amount, it is hard not to remark on the incredible success of Google and Amazon, just making an incredible amount of money during that dotcom oil rush.

Facebook, on the other hand, was the poster-child of the growth-over-revenue movement, so it’s hard to understand exactly what the slowing of growth there means – as their growth in users at that time was still accelerating.


In each of these cases, it’s possible, with a bit of poetic license, to interpret the growth stories of each of these companies (or groups). To make sense, in hindsight, of their growth journey and why it was or wasn’t exceptional. That’s already good advice to founders who are looking at projecting into the future: how can you make your future growth story cohesive with your projections.

  • If you are aiming to hit $100M in ARR one day, you have a path laid out by Hockey Stick Principles for what that kind of growth looks like, as well as what it looks like – even for a successful startup – if you aren’t so ambitious.
  • If you are projecting Google-like growth, can you really make the argument that your product is so fundamentally transformative that you’ll have free reign over such a large blue-ocean opportunity? If so, how can you sensibly put a cap on your growth projections?
  • If you are building in a traditional industry, where there may be fewer unknowns but more barriers in terms of regulation and competition, are you going to need to consider AirBnB’s journey? How do you strike a balance between an accessible customer base and (potentially) the need to outmaneuver others to reach them?

(* Linear graphs have a consistent scale, representing uniform progression (e.g., 1, 2, 3), while logarithmic graphs use a multiplicative scale, often showcasing data that spans multiple orders of magnitude (e.g., 1, 10, 100). The choice between the two effects data visualization and interpretation, with linear graphs highlighting additive changes and logarithmic graphs emphasizing exponential shifts.)